Underactuated USV Fixed-Time Trajectory Tracking Using Neural Network and Sliding Mode Control

Abstract

In recent years, underactuated unmanned surface vehicles have attracted considerable research interest. These vehicles exhibit underactuated characteristics, meaning the independent control inputs are outnumbered by the degrees of freedom. This characteristic poses significant challenges to controller design. Furthermore, unmodeled dynamics within the system and external ocean disturbances further complicate the controller design process. This paper proposes a fixed-time control scheme for underactuated unmanned surface vehicles. First, the trajectory tracking error is reformulated by using center-of-gravity shift technique (CGST). Second, to handle unknown marine disturbances, a radial basis function neural network (RBFNN) is established, complemented by the the minimum learning parameter (MLP) method to lower the controller’s computational complexity. Third, a fixed-time sliding mode control (FTSMC) strategy is developed to achieve fixed-time convergence of the tracking error to a bounded set. Finally, simulations demonstrate the performance of the overall approach.